Analyzing the influence of the sampling rate in the detection of malicious traffic on flow data

[EN] Cyberattacks are a growing concern for companies and public administrations. The literature shows that analyzing network-layer traffic can detect intrusion attempts. However, such detection usually implies studying every datagram in a computer network. Therefore, routers routing a significant volume of network traffic do not perform an in-depth analysis of every packet. Instead, they analyze traffic patterns based on network flows. However, even gathering and analyzing flow data has a high-computational cost, and therefore routers usually apply a sampling rate to generate flow data. Adjusting the sampling rate is a tricky problem. If the sampling rate is low, much information is lost and some cyberattacks may be neglected, but if the sampling rate is high, routers cannot deal with it. This paper tries to characterize the influence of this parameter in different detection methods based on machine learning. To do so, we trained and tested malicious-traffic detection models using synthetic flow data gathered with several sampling rates. Then, we double-check the above models with flow data from the public BoT-IoT dataset and with actual flow data collected on RedCAYLE, the Castilla y León regional academic network.S

SQL injection attack detection in network flow data

[EN] SQL injections rank in the OWASP Top 3. The literature shows that analyzing network datagrams allows for detecting or preventing such attacks. Unfortunately, such detection usually implies studying all packets flowing in a computer network. Therefore, routers in charge of routing significant traffic loads usually cannot apply the solutions proposed in the literature. This work demonstrates that detecting SQL injection attacks on flow data from lightweight protocols is possible. For this purpose, we gathered two datasets collecting flow data from several SQL injection attacks on the most popular database engines. After evaluating several machine learning-based algorithms, we get a detection rate of over 97% with a false alarm rate of less than 0.07% with a Logistic Regression-based model.SIInstituto Nacional de Ciberseguridad de España (INCIBE)Universidad de Leó

SEPAR Recommendations for COVID-19 Vaccination in Patients With Respiratory Diseases

[ES] La Sociedad Española de Neumología y Cirugía Torácica (SEPAR) ha elaborado este documento de recomendaciones sobre la vacuna para la COVID-19 en las enfermedades respiratorias, con el objetivo de ayudar al personal sanitario en la toma de decisiones sobre cómo actuar en la vacunación de estos pacientes. Las recomendaciones han sido elaboradas por un grupo de expertos en la materia, tras la revisión de la literatura recopilada hasta el 7 de marzo del 2021, y de la información aportada por distintas sociedades científicas, agencias del medicamento y estrategias de organismos gubernamentales hasta esa fecha. Podemos concluir que las vacunas para la COVID-19 no solo son seguras y eficaces, sino que, en aquellos pacientes vulnerables con enfermedades respiratorias crónicas, son prioritarias. Además, la implicación activa de los profesionales sanitarios que manejan estas patologías en la estrategia de vacunación es clave para lograr una buena adherencia y coberturas vacunales elevadas.[EN] The Spanish Society of Pneumonology and Thoracic Surgery (SEPAR) has elaborated this document of recommendations for COVID-19 vaccination in patients with respiratory diseases aimed to help healthcare personnel make decisions about how to act in case of COVID-19 vaccination in these patients. The recommendations have been developed by a group of experts in this field after reviewing the materials published up to March 7, 2021, the information provided by different scientific societies, drug agencies and the strategies of the governmental bodies up to this date. We can conclude that COVID-19 vaccines are not only safe and effective, but also prior in vulnerable patients with chronic respiratory diseases. In addition, an active involvement of healthcare professionals, who manage these diseases, in the vaccination strategy is the key to achieve good adherence and high vaccination coverage

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)

VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad

Acta de congresoLa conmemoración de los cien años de la Reforma Universitaria de 1918 se presentó como una ocasión propicia para debatir el rol de la historia, la teoría y la crítica en la formación y en la práctica profesional de diseñadores, arquitectos y urbanistas. En ese marco el VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad constituyó un espacio de intercambio y reflexión cuya realización ha sido posible gracias a la colaboración entre Facultades de Arquitectura, Urbanismo y Diseño de la Universidad Nacional y la Facultad de Arquitectura de la Universidad Católica de Córdoba, contando además con la activa participación de mayoría de las Facultades, Centros e Institutos de Historia de la Arquitectura del país y la región. Orientado en su convocatoria tanto a docentes como a estudiantes de Arquitectura y Diseño Industrial de todos los niveles de la FAUD-UNC promovió el debate de ideas a partir de experiencias concretas en instancias tales como mesas temáticas de carácter interdisciplinario, que adoptaron la modalidad de presentación de ponencias, entre otras actividades. En el ámbito de VIII Encuentro, desarrollado en la sede Ciudad Universitaria de Córdoba, se desplegaron numerosas posiciones sobre la enseñanza, la investigación y la formación en historia, teoría y crítica del diseño, la arquitectura y la ciudad; sumándose el aporte realizado a través de sus respectivas conferencias de Ana Clarisa Agüero, Bibiana Cicutti, Fernando Aliata y Alberto Petrina. El conjunto de ponencias que se publican en este Repositorio de la UNC son el resultado de dos intensas jornadas de exposiciones, cuyos contenidos han posibilitado actualizar viejos dilemas y promover nuevos debates. El evento recibió el apoyo de las autoridades de la FAUD-UNC, en especial de la Secretaría de Investigación y de la Biblioteca de nuestra casa, como así también de la Facultad de Arquitectura de la UCC; va para todos ellos un especial agradecimiento

Contributions of deep learning to people detection, tracking and recognition in service robots

158 p.[ES] Los robots sociales, tienen como objetivo interactuar con las personas en todo tipo de entornos. Esta interacción puede desarrollarse en diferentes escenarios, desde que el robot proporcione información, hasta que resuelva una tarea concreta. Los robots asistenciales son un subconjunto de los robots sociales, cuya finalidad es ayudar a las personas en entornos de restauración, domésticos, hospitalarios, etc. Las tareas que deben afrontar los robots asistenciales añaden a la complejidad de la robótica general, la necesidad de interactuar con los humanos, que esperan que su rendimiento sea similar al que realizaría un humano en un entorno doméstico o de atención al cliente. Además, estos robots deben funcionar de forma autónoma, es decir, deben tener la capacidad de tomar sus propias decisiones por sí mismos en el entorno en el que estén desplegados. Dentro de los problemas clásicos de la robótica de servicios se encuentra la ”navegación”, es decir, la habilidad para desplazarse por el entorno de forma autónoma y sin dañar objetos o personas que se encuentren en su trayectoria. Es habitual que las soluciones de navegación traten de la misma forma a personas y objetos a la hora de desplazarse por el entorno, lo cual no es apropiado en el caso de robots asistenciales. Al problema específico de la navegación de robots autónomos en entornos con personas se le denomina ”navegación social”, que es el problema que se aborda en esta tesis. La navegación social no solo hace referencia a evitar colisionar con las personas u objetos que haya en la trayectoria y el cálculo de la misma, también tiene que ver con la capacidad del robot para aproximarse a una persona, caminar junto a ella, seguirla, etc. Dichas capacidades, están estrechamente relacionados con tres habilidades esenciales: la detección, el seguimiento y el reconocimiento de personas. El objetivo de esta tesis doctoral se puede resumir en el desarrollar de métodos que permitan la creación de un pipeline de detección, seguimiento y reconocimiento de personas que se pueda integrar en sistemas de navegación social, lo que fomentará la interacción persona-robot y facilitará la aceptación de estos robots en todo tipo de entornos. Para ello, en el desarrollo de la tesis se han propuesto y evaluado una serie de métodos que se han integrado en dos sistemas. En primer lugar, el sistema que hemos denominado People Tracking (PeTra), que permite llevar a cabo la detección y seguimiento de las personas en las inmediaciones del robot. En segundo lugar, Biometric RecognITion Through gAit aNalYsis (BRITTANY) permite llevar a cabo el reconocimiento de las personas por su forma de caminar. Ambos sistemas se basan únicamente en la información contenida en rejillas de ocupación que pueden proporcionar diferentes sensores. PeTra se basa en el uso de una Convolutional Neural Network (CNN) de segmentación, que mediante el procesamiento de un mapa de ocupación, permite determina aquellos puntos de la rejilla de ocupación que corresponden con una persona presente en la escena. A partir de esa información, mediante el post-procesamiento de los datos, se puede realizar el seguimiento de las personas. Para ello se plantean dos posibles aproximaciones: el cálculo de distancias euclídeas y el uso de filtros de Kalman. PeTra ha sido comparado con Leg Detector (LD), la solución por defecto presente en Robot Operating System (ROS), basada en Random Trees para determinar la ubicación de las personas. El sistema final, ha reportado mejores resultados en materia de detección que LD. Y en materia de seguimiento, la aproximación mediante filtros de Kalman también ha reportado mejores resultados que la implementación mediante el cálculo de distancias euclídeas. BRITTANY se basa en el uso de una CNN de clasificación, que mediante el procesamiento de un mapa de ocupación agregado, permite determinar que usuario se encuentra caminando delante del robot. El mapa de ocupación agregado se crea mediante la concatenación de varios mapas de ocupación que solo contienen aquellos puntos del sensor que forman parte de una persona. Mediante la concatenación de estos mapas, se consigue representar la acción de caminar de una persona. BRITTANY propone una nueva arquitectura de CNN que se ha comparado con arquitecturas de clasificación conocidas como LeNet o AlexNet. El sistema final desarrollado es robusto incluso ante usuarios externos al sistema.[EN] Social robots aim to interact with people in all kinds of environments. This interaction can occur in different scenarios, from the robot providing information to solving a specific task. Assistive robots are a subset of social robots whose purpose is to help people in restaurants, homes, hospitals, etc. Tasks faced by assistive robots add to the complexity of general robotics, the need to interact with humans, who expect their performance to be similar to that of a human in a domestic or customer service environment. In addition, these robots must operate autonomously, i.e. they must have the ability to make their own decisions on their own in the environment in which they are deployed. Tasks faced by assistive robots add to the complexity of general robotics, the need to interact with humans, who expect their performance to be similar to that of a human in a domestic or customer service environment. In addition, these robots must operate autonomously, i.e. they must have the ability to make their own decisions on their own in the environment in which they are deployed. One of the classic problems in service robotics is "navigation", i.e. the ability to move through the environment autonomously and without damaging objects or people in its path. It is common for navigation solutions to treat people and objects in the environment in the same way, which is not appropriate for assistive robots. The specific problem of autonomous robot navigation in human environments is called "social navigation", which is the problem addressed in this thesis. Social navigation is not only about avoiding collisions with people or objects in the trajectory and the calculation of the trajectory, it is also about the robot’s ability to approach a person, walk next to him/her, follow him/her, etc. These capabilities are closely related to three essential skills: detection, tracking and recognition of people. The objective of this PhD can be summarised as the development of methods that allow the creation of a human detection, tracking and recognition pipeline that can be integrated into social navigation systems, which will promote human-robot interaction and facilitate the acceptance of these robots in all kind of environments. For this purpose, in the development of the PhD, a series of methods have been proposed and evaluated, which have been integrated into two systems. Firstly, the system called People Tracking (PeTra), enables the detection and tracking of people in the vicinity of the robot. Secondly, Biometric RecognITion Through gAit aNalYsis (BRITTANY) enables the recognition of people by their gait analysis. Both systems rely solely on the information contained in occupancy maps that can be provided by different sensors. People Tracking (PeTra) is based on the use of a segmentation Convolutional Neural Network (CNN), which through the processing of an occupancy map, allows to determine which points of the occupancy map belong to a person. Based on this information, by post-processing the data, the persons can be tracked. Two possible approaches are considered: the calculation of Euclidean distances and the use of Kalman filters. PeTra has been compared with Leg Detector (LD), the default solution present in Robot Operating System (ROS), based on em Random Trees to determine the location of people. The final system has reported better detection results than LD. And in terms of tracking, the Kalman filter approach has also reported better results than the implementation using Euclidean distance calculation. BRITTANY is based on the use of a classification CNN, which by processing an aggregated occupancy map, allow to determine which user is walking in front of the robot. The aggregated occupancy map is created by concatenating several occupancy maps that contain only those sensor points that are part of a person. By concatenating these maps, the walking action of a person is represented. BRITTANY proposes a new architecture of CNN that has been compared with two well-known classification architectures, LeNet and AlexNet. The final system developed is robust even to users outside the system

Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people’s location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one—where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%

People Detection and Tracking Using LIDAR Sensors

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League

Malicious traffic detection on sampled network flow data with novelty-detection-based models

Abstract Cyber-attacks are a major problem for users, businesses, and institutions. Classical anomaly detection techniques can detect malicious traffic generated in a cyber-attack by analyzing individual network packets. However, routers that manage large traffic loads can only examine some packets. These devices often use lightweight flow-based protocols to collect network statistics. Analyzing flow data also allows for detecting malicious network traffic. But even gathering flow data has a high computational cost, so routers usually apply a sampling rate to generate flows. This sampling reduces the computational load on routers, but much information is lost. This work aims to demonstrate that malicious traffic can be detected even on flow data collected with a sampling rate of 1 out of 1,000 packets. To do so, we evaluate anomaly-detection-based models using synthetic sampled flow data and actual sampled flow data from RedCAYLE, the Castilla y León regional subnet of the Spanish academic and research network. The results presented show that detection of malicious traffic on sampled flow data is possible using novelty-detection-based models with a high accuracy score and a low false alarm rate